Abstract
Data mining methods focus on discovering models and patterns from large databases that summarize the data. However, generating such results is not an end in itself because their applicability is not straightforward. Ideally, the user would ultimately like to use them to decide what actions to take. Action mining explicitly emerged as a response to this need. Currently, most of the action mining methods rely on simple data which describes each object independently that means they do not take into account relationships between objects. In social networks, relationships enable an individual to influence another one, so ignoring them in action mining process would lead to miss some profitable actions. In this paper, we introduce action mining from social networks. In fact, our main contribution is to extract cost-effective actions which is formulated as an optimization problem where the objective is to learn actions consisting of the changes in the network that are likely to result in desired changes in the labels of intended individuals while minimizing the cost of the changes. Experiments confirm that the proposed approach performs much better than the current state-of-the-art in action mining.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Alam, S., & Alam, M. (2012). Actionable knowledge mining from improved post processing decision trees, inter-national conference on computing and control engi-neering (ICCCE 2012). Chennai, pp. 1–8.
Anagnostopoulos, A., Kumar, R., Mahdian, M. (2008). Influence and correlation in social networks. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’08).
Bhagat, S., Cormode, G., Muthukrishnan, S. (2011). Node classification in social networks. In Social network data Analytics (pp. 115–148). Springer, US.
Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X. (2006). Group formation in large social networks: membership, growth, and evolution. In Proceedings of 12th international conference on knowledge discovery in data mining. New York (pp. 44–54 ).
Bagavathi, A., Mummoju, P., Tarnowska, K., Tzacheva, A.A, Ras, Z.W. (2017). SARGS method for distributed actionable pattern mining using spark. In 2017 IEEE international conference on big data (big data) (pp. 4272–4281).
Cao, L. (2012). Actionable knowledge discovery and delivery. WIREs Data Mining and Knowledge Discovery, 2(2), 149–163.
Ching-Lai, H., & Abu Syed Md, M. (1979). Multiple objective decision making, methods and applications: a state-of-the-art survey. Lecture notes in economics and mathematical systems, 164. Springer.
Cao, L., Zhao, Y., Zhang, H., Luo, D., Zhang, C., Park, E.K. (2010). Flexible frameworks for actionable able knowledge discovery. IEEE Transactions on Knowledge and Data Engineering, TKDE, 22, 1299–1312.
Held, M., & Karp, R. (1970). The traveling-salesman problem and minimum spanning trees. Operations Research, 18, 1138–1162.
Hajja, A., Ras, Z.W., Wieczorkowska, A. (2014). Hierarchical object-driven action rules. Journal of Intelligent Information Systems, 42(2), 207–232. Springer.
He, Z., Xu, X., Deng, S. (2003). Data mining for actionable knowledge: a survey. LNCS (LNAI), 3918, 821–830.
He, Z., Xu, X., Deng, S., Ma, R. (2005). Mining action rules from scratch. Expert Systems with Applications, 29(3), 691–699.
Im, S., Ras, Z.W., Wasyluk, H. (2010). Action rule discovery from incomplete data. Knowledge and Information Systems Journal, 25, 1, 21–33. Springer.
Ionescu, C., Vantzos, O., Sminchisescu, C. (2015). Training deep networks with structured layers by matrix backpropagation. In Proceedings of international conference on computer vision, ICCV 2015.
Kalanat, N., Shamsinejad, P., Saraee, M. (2015). A fuzzy method for discovering cost-effective actions from data. Journal of Intelligent and Fuzzy Systems, 28(2), 757–765.
Kalanat, N., & Minaei, B. (2016). An optimized fuzzy method for finding optimal actions. Journal of Intelligent Information Systems, pp. 1–9.
Petersen, K.B., & Pedersen, M.S. (2012). The matrix cookbook. Technical University of Denmark.
Ruder, S. (2016). An overview of gradient descent optimization algorithms, BMC proceedings. Insight Centre for Data Analytics, NUI Galway Aylien Ltd., Dublin.
Ras, Z., & Dardzinska, A. (2011). From data to classification rules and actions. International Journal of Intelligent Systems, 26(6), 572–590. Wiley.
Ras, Z., & Tsay, L. (2003). Discovering extended action-rules (System DEAR). Intelligent Information Systems, IIS03 Symposium, pp. 293–300.
Ras, Z.W., Tarnowska, K., Kuang, J., Daniel, L., Fowler, D. (2017). User friendly NPS-based recommender system for driving business revenue. Proceedings of 2017 international joint conference on rough sets (IJCRS’17), LNCS, 10313, 34–48. Springer.
Ras, Z., & Wieczorkowska, A. (2000). Action rules: How to increase profit of a company. Proceedings of PKDD00, LNAI, 1910, 587–5922.
Shamsinejadbabaki, P. (2014). Causal action mining, Thesis Isfahan University of Technology.
Su, P., & Mao, W. (2015). Power-function-based observation-weighting method for mining actionable behavioral rules.
Su, P., Mao, W., Zeng, D., Zhao, H. (2012). Mining actionable behavioral rules. Decision Support Systems, 54, 142–152.
Subraman, S., Wang, H., Balasubramaniam, S., Zhou, R., Ma, J., Zhang, Y., Whittaker, F., Zhao, Y., Rangarajan, S. (2016). Mining actionable knowledge using reordering based diversified actionable decision trees, WISE 2016, Part I. LNCS, 10041, 553–560.
Tzacheva, A., Bagavathi, A., Ganesan, P. (2016). MR-Random forest algorithm for distributed action rules discover. International Journal of Data Mining and Knowledge Management Process (IJDKP) 6(5).
Tolomei, G., Silvestri, F., Haines, A., Lalmas, M. (2017). Interpretable predictions of tree-based ensembles via actionable feature tweaking. In Proceeding KDD ’17 Proceedings 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 465–474).
Yang, Q., Yin, J., Ling, C., Pan, R. (2007). Extracting actionable knowledge from decision trees. IEEE Transactions on Knowledge and Data Engineering, 18(12), 43–56.
Zhu, X. (2015). Machine teaching: an inverse problem to machine learning and an approach toward optimal education. In The twenty-ninth AAAI conference on artificial intelligence (AAAI “Blue Sky” Senior Member Presentation Track), AAAI / Computing Com.
Zafarani, R., Abbasi, M., Liu, H. (2014). Social media mining: an introduction. Cambridge: Cambridge University Press.
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B. (2004). Learning with local and global consistency. Advances in Neural Information Processing Systems, 16(16), 321–328.
Zhu, X., Singla, A., Zilles, S., Rafferty, A. (2018). An overview of machine teaching. arXiv:1801.05927.
Zhicheng, C., Wenlin, C., He, Y., Yixin, C. (2015). Optimal action extraction for random forests and boosted trees. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 179–188).
Zeng, D., Wang, L., Zeng, D. (2015). An observation-weighting method for mining actionable behavioral rules. In Proceedings of ICACI.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kalanat, N., Khanjari, E. Action extraction from social networks. J Intell Inf Syst 54, 317–339 (2020). https://doi.org/10.1007/s10844-019-00551-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10844-019-00551-2